Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

By M. Shehata, F. Khalifa, A. Soliman, M. Ghazal, F. Taher, M. El-Ghar, Amy C. Dwyer, G. Gimel'farb, Robert S. Keynton, Ayman S. El-Baz. 2019

40 citations recorded by the source corpus.

<italic>Objective:</italic> Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1–2 weeks), and potential for complications, e.g., bleeding and/or infection. <italic>Methods:</italic> This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + <inline-formula><tex-math notation="LaTeX">$b$</tex-math></inline-formula>-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov–Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different <inline-formula><tex-math notation="LaTeX">$b$</tex-math></inline-formula>-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants. <italic>Results:</italic> In our initial “leave-one-subject-out” experiment on 100 subjects, <inline-formula><tex-math notation="LaTeX">$\text{97.0}\%$</tex-math></inline-formula> of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of <inline-formula><tex-math notation="LaTeX">$\text{96.0}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{94.0}\%$</tex-math></inline-formula>, respectively. <italic>Conclusion:</italic> These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection. <italic>Significance:</italic> The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.

Subfields: Radiology, Nuclear Medicine and Imaging, Surgery, Transplantation

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Arvid Lundervold

Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “Renal transplant [286] A deep-learning-based classifier with stacked non-negative constrained autoencoders to distinguish between”
Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “ber of approaches have also used Auto-Encoders (AE) [60], [61].”
Deep learning in medical imaging and radiation therapy. (2018, background): “Shehata et al. 276 used autoencoders for early detection/ prediction of acute renal rejection after kidney transplant.”